Introduction – Visualization of musics allows you to analyze features of music that would be considered subjective when only able to listen to music.

Administrative - working in Python – a lot of useful libraries available especially for plotting data, use of the .wav file format

Frequency Domain – the Fourier Transform takes a function of time and transforms it to a function of frequency

Spectrogram – A visual representation of how the frequencies in a signal vary with time. Represented by a heat map where change in colour represents change in intensity of a frequency. Frequency and time are represented on the traditional axes. Show some examples and describe interesting features.

Generating a Spectrogram – show documentation for matplotlib specgram() function, describe what it does, show example code

Power plots – Shows how the power of the signal is distributed among the frequencies present. Only makes sense for signals which do not vary over time such as individual notes. Show some examples including trumpet notes where power of higher harmonics exceed fundamental frequencies.

For the trumpet fanfare recording, thanks to [ Ссылка ]

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